Generative flow networks [1]} are generative models that construct objects lying in a target space \(\mathcal {X}\)  by taking sequences of actions sampled from a learned policy. GFlowNets are trained so as to make the probability of sampling an object \(x\in \mathcal {X}\)  proportional to a given nonnegative reward \(R(x)\) . GFlowNets' use of a parametric policy that can generalize to states not seen during training makes them a competitive alternative to methods based on local exploration in various probabilistic modeling tasks [1]}, [3]}, [4]}, [5]}, [6]}.
